Neuro Symbolic Learning with Differentiable Inductive Logic Programming

In this chapter, we describe how a logic program can be learned from data in a neuro symbolic framework. Our focus is on the gradient-based method known as differentiable inductive logic programming (ILP), which combines concepts from ILP with a neural architecture to support gradient-based learning. Additionally, we also cover several other paradigms to learn logical structures in a neuro symbolic framework.